Build a fast deep learning machine for under $1K • Hacker News Board

Re: Build a fast deep learning machine for under $1K

#61

Post by mamcx » Fri, Feb 10, 2017, 3:08 AM UTC

lloyd-christmas wrote: post #46

mamcx wrote: post #25

Earlier quoted context omitted.

Was years ago when I build my last PC and I'm out of the game. I'm tempted in build a hackintosh: https://www.tonymacx86.com/threads/hackintosh-cutting-edge-k... - Intel i7 Kaby Lake - No decided on motherboard. The one that cause me less trouble (for hackintosh) is fine. - GTI 750ti (have) or buy a pascal nvidia. - NVMe drive if possible - 32 GB RAM. - Probably a Thermaltake CORE P3 case. Not decided. I was thinking…

Just FYI, 10 series cards aren't supported by hackintosh currently (and for the foreseeable future).

Yep, the parts I wish to use need some hacking. Supposedly kaby lake + nvme can be made to work. I don't need 10 cards, I can keep the 750ti

Re: Build a fast deep learning machine for under $1K

#62

Post by croon » Fri, Feb 10, 2017, 10:06 AM UTC

mamcx wrote: post #60

croon wrote: post #32

Earlier quoted context omitted.

> I was thinking in use a Liquid Cooler but wonder if the Noctua could be better/less noise? Depends, is your option of water cooling an All-In-One-solution that have become popular in recent years? Their performance is on par or slightly better than a large heat sink + large low speed fan(s), but they're not generally quieter, as you still have fans for them, as well as a pump. I considered those options when buildi…

I was planning in use Corsair Hydro H60 ( http://amzn.to/2k9a9O1 ). However, I wish to have a quiet system, and my brother have it and it sound louder than I wish. I don't plan on overcloking.

Yeah, that's one of those AIO water coolers I was referring to.

http://www.anandtech.com/show/5054/corsair-hydro-series-h60-...

The "Silver Arrow" is an air cooler from Thermalright that is pretty equivalent to a Noctua or a Phantek or be Quiet! etc.

In that review it beats the H60 in both temperature and noise. The H60 is more than twice as loud.

So yes, unless you assemble your own water cooling system, I'd say definitely go for a regular heatsink+fan.

Re: Build a fast deep learning machine for under $1K

#63

Post by tripzilch » Fri, Feb 10, 2017, 1:20 PM UTC

dsacco wrote: post #33

I think there should be a disclaimer here that while, yes, this will work , it will quickly become suboptimal (perhaps even dysfunctional) for a lot of work as you scale your hobby into something resembling more professional work. I personally would not start with a thousand dollar machine if you have any intention of doing serious computation in the future. While you can trade out parts pretty easy on a custom built…

So what you're saying is that if you start with the thing in this article, and then want to upgrade to the thing you described, don't expect it to cost $9k, it'll cost $10k :-)

(and as a bonus leftover: a nice machine to donate your local volunteer hackerspace, youth tech center, school etc etc)

I don't see the problem, as long as it's roughly 9-10x faster for an embarrassingly-parallel task of larger size, that 10% isn't going to make a big dent, is it? :-)

Re: Build a fast deep learning machine for under $1K

#64

Post by tripzilch » Fri, Feb 10, 2017, 3:41 PM UTC

web007 wrote: post #40

dsacco wrote: post #33

I think there should be a disclaimer here that while, yes, this will work , it will quickly become suboptimal (perhaps even dysfunctional) for a lot of work as you scale your hobby into something resembling more professional work. I personally would not start with a thousand dollar machine if you have any intention of doing serious computation in the future. While you can trade out parts pretty easy on a custom built…

Are multi-GPU systems worth it until you have a TON of experience building parallel models? Almost all of the reference architectures I've seen are essentially serial, and TF doesn't have good data-parallelism built in to make use of N GPUs, N > 1.

Maybe this is a stupid idea, but if you had two GPUs, doesn't that basically mean, in terms of good parallelism, that at least you can train two models/networks at the same time, roughly?

Because as long as you're in the research and development phase, that'll help cut your coding/training/testing/adjusting cycle. I assume you'll be tuning your hyperparameters, perhaps on somewhat smaller test models, but they will still take half a day or so to train? That means you can try (almost) twice as many hyperparameter configurations in the same time. It still helps to spin up a second test with a different selection of parameters, even if you haven't gotten the results back from the first test, right?

Re: Build a fast deep learning machine for under $1K

#65

Post by lloyd-christmas » Fri, Feb 10, 2017, 4:46 PM UTC

mamcx wrote: post #61

lloyd-christmas wrote: post #46

Earlier quoted context omitted.

Just FYI, 10 series cards aren't supported by hackintosh currently (and for the foreseeable future).

Yep, the parts I wish to use need some hacking. Supposedly kaby lake + nvme can be made to work. I don't need 10 cards, I can keep the 750ti

I run one with a 5930k, 980ti, nvme. It had huge issues with el capitan, but sierra has been a breeze. The pascal part is an issue, but running a small card/integrated graphics for the screen is perfectly fine if you're dedicating the GPU to computing.